Paper Info
Title
Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression
Abstract
Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behavior, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.
Year
DOI
Venue
2014
10.1145/2661806.2661812
AVEC@MM
Keywords
Field
DocType
depression recognition,health,challenge,beck depression inventory,facial expression,affective computing,vision and scene understanding,inventory
Social psychology,Histogram,Mood,Audio signal,Feature vector,Pattern recognition,Partial least squares regression,Psychology,Facial expression,Beck Depression Inventory,Artificial intelligence,Affective computing
Conference
Citations 
PageRank 
References 
23
0.86
24
Authors
5
Name
Order
Citations
PageRank
Asim Jan1513.43
Hongying Meng283269.39
Yona Falinie A. Gaus3403.87
Fan Zhang422969.82
Saeed Turabzadeh5231.20